Ed Baskerville last update: 26 February 2015
NB: Usage instructions out of date but repo is published for the sake of reproducibility for paper.
This package contains a Python implementation of nonlinear time-series embedding methods.
Requires Python 2.7.x with numpy and scipy.
Recommended way to install Python:
After you have installed Anaconda and configured your PATH environment variable, make
sure that
$ python --version
includes the word "Anaconda" in the description.
The script ccm.py automates the method of convergent cross-mapping (TODO: ref Sugihara et al.).
Run
ccm.py --help
to see detailed usage information.
Example:
ccm.py --n-cores 4 -E 4 -t 1 -L "5:50:5" -R 100 -C "x,y" -V "x:y,y:x" input.sqlite timeseries output.sqlite
will read time-series data from columns x and y (-C "x,y") in table timeseries
in the SQLite database input.sqlite; test causality for x causing y as well as
y causing x; and write output to the file output.sqlite; using these parameters:
- embedding dimension: 4 (
-E 4) - lag (tau): 1 (
-t 1) - library lengths: 5, 10, ..., 45, 50 (
-L "5:50:5") - replicate samples per library length: 100 (
-R 100)
The output database will contain two tables:
args: contains the arguments used to run the programresults: contains analysis results.
The results table contains these columns:
cause: the variable being tested as causeeffect: the variable being tested as effectL: the library length being testedreplicate_id: unique ID for this replicatecorr: the correlation between the original "cause" time series and the version reconstructed from lagged vectors sampled from "effect".
TODO